Machine Learning Approaches for Prediction of various Cancer types

Sanjay Garag¹ , Anupama S. Nandeppanavar² , Medha Kudari³

1. Dept of MCA, KLE Institute of Technology, Hubballi, India.
2. Dept of MCA, KLE Institute of Technology, Hubballi, India.
3. Dept of MCA, KLE Institute of Technology, Hubballi, India.

Section:Research Paper, Product Type: Journal-Paper
Vol.10 , Issue.6 , pp.1-8, Dec-2022

Online published on Dec 31, 2022

Copyright © Sanjay Garag, Anupama S. Nandeppanavar, Medha Kudari . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
Cancer is a prevalent disease that affects the people and an early diagnosis will expedite the treatment of this ailment. The Machine Learning is providing enormous contribution to the biomedical field. The main goal of this project is to build a model for predicting cancer using support vector machine classification algorithms. Compare the accuracy of different kernels and apply different parameters to one efficient kernel. Cancer is characterized as a heterogeneous disease of many different subtypes. The Cancer Disease Prediction contains the machine learning models like Random Forest Classifier, Support Vector Machine, K-Nearest Neighbor (KNN), K-Means Clustering, Decision Tree Algorithm and then the collected data is pre-processed using some machine learning techniques. Data divided into the training data and the testing data. Then the Machine Learning Algorithm applied to yield the significant results. The analysis with Decision Tree Algorithm gives the best results for predicting the type of the cancer by considering the symptoms that the patients are bearing. The system is developed to predict that the person is having a cancer or not before going for the lab tests.

Key-Words / Index Term :
Random Forest, Support Vector Machine, K-Nearest Neighbor, K-Means Clustering, Decision Tree, Prediction, kernel

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